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Abstract

Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, that is, if they always agree, then the committee is unnecessary—any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

Background

A supervised machine learner constructs a mapping from input data (normally described by several features) to the appropriate outputs. It does this by learning from a training set—N inputs x1, x2, …, xN for which the corresponding true outputs y1, y2, …, yN are known. The model that results is used to map new inputs to the appropriate outputs. In a classification learning task, each output is one or more classes to which the input belongs. The goal of classification learning is to develop a model that separates the data into the different classes, with the aim of classifying new examples in the future. For example, a credit card company may develop a model that separates people who defaulted on their credit cards from those who did not based on other known information such as annual income. A model would be generated based on data from past credit card holders. The model would be used to predict whether a new credit card applicant is likely to default on his credit card and thereby decide whether to approve or deny this applicant a new card. In a regression learning task, each output is a continuous value to be predicted (e.g., the average balance that a credit card holder carries over to the next month).

Many traditional machine learning algorithms generate a single model (e.g., a decision tree or neural network). Ensemble learning methods instead generate multiple models. Given a new example, the ensemble passes it to each of its multiple base models, obtains their predictions, and then combines them in some appropriate manner (e.g., averaging or voting). As mentioned earlier, it is important to have base models that are competent but also complementary (Tumer and Ghosh, 1996). To further motivate this point, consider Figure 1. This figure depicts a classification problem in which the goal is to separate the points marked with plus signs from points marked with minus signs. None of the three individual linear classifiers (marked A, B, and C) is able to separate the two classes of points. However, a majority vote over all three linear classifiers yields the piecewise-linear classifier shown as a thick line. This classifier is able to separate the two classes perfectly. For example, the plusses at the top of the figure are correctly classified by A and B, but are misclassified by C. The majority vote over these correctly classifies these points as plusses. This happens because A and B are very different from C. If our ensemble instead consisted of three copies of C, then all three classifiers would misclassify the plusses at the top of the figure, and so would a majority vote over these classifiers.

Figure 1.

An ensemble of linear classifiers. Each line—A, B, and C—is a linear classifier. The boldface line is the ensemble that classifies new examples by returning the majority vote of A, B, and C